{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pwlf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24d81ae6f28>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 1024, 100)\n",
    "y = np.sin(.01*x)\n",
    "plt.figure()\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from GPyOpt.methods import BayesianOptimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_pwlf = pwlf.PiecewiseLinFit(x, y, degree=2)\n",
    "number_of_line_segments = 4\n",
    "my_pwlf.use_custom_opt(number_of_line_segments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define domain for possible breakpoints\n",
    "bounds = [{'name': 'break_1', 'type': 'discrete',\n",
    "           'domain': np.arange(1, 1023)},\n",
    "          {'name': 'break_2', 'type': 'discrete',\n",
    "           'domain': np.arange(1, 1023)},\n",
    "          {'name': 'break_3', 'type': 'discrete',\n",
    "           'domain': np.arange(1, 1023)}]\n",
    "max_iter = 120"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_obj(x):\n",
    "    f = np.zeros(x.shape[0])\n",
    "    for i, j in enumerate(x):\n",
    "        f[i] = my_pwlf.fit_with_breaks_opt(j)\n",
    "    return f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "myBopt = BayesianOptimization(my_obj,\n",
    "                              domain=bounds, model_type='GP',\n",
    "                              initial_design_numdata=20,\n",
    "                              initial_design_type='latin',\n",
    "                              exact_feval=True, verbosity=True,\n",
    "                              verbosity_model=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num acquisition: 1, time elapsed: 0.20s\n",
      "num acquisition: 2, time elapsed: 0.40s\n",
      "num acquisition: 3, time elapsed: 0.60s\n",
      "num acquisition: 4, time elapsed: 0.79s\n",
      "num acquisition: 5, time elapsed: 0.97s\n",
      "num acquisition: 6, time elapsed: 1.15s\n",
      "num acquisition: 7, time elapsed: 1.35s\n",
      "num acquisition: 8, time elapsed: 1.53s\n",
      "num acquisition: 9, time elapsed: 1.73s\n",
      "num acquisition: 10, time elapsed: 1.93s\n",
      "num acquisition: 11, time elapsed: 2.11s\n",
      "num acquisition: 12, time elapsed: 2.30s\n",
      "num acquisition: 13, time elapsed: 2.49s\n",
      "num acquisition: 14, time elapsed: 2.76s\n",
      "num acquisition: 15, time elapsed: 3.01s\n",
      "num acquisition: 16, time elapsed: 3.28s\n",
      "num acquisition: 17, time elapsed: 3.51s\n",
      "num acquisition: 18, time elapsed: 3.79s\n",
      "num acquisition: 19, time elapsed: 4.08s\n",
      "num acquisition: 20, time elapsed: 4.32s\n",
      "num acquisition: 21, time elapsed: 4.57s\n",
      "num acquisition: 22, time elapsed: 4.83s\n",
      "num acquisition: 23, time elapsed: 5.08s\n",
      "num acquisition: 24, time elapsed: 5.34s\n",
      "num acquisition: 25, time elapsed: 5.66s\n",
      "num acquisition: 26, time elapsed: 5.95s\n",
      "num acquisition: 27, time elapsed: 6.20s\n",
      "num acquisition: 28, time elapsed: 6.49s\n",
      "num acquisition: 29, time elapsed: 6.74s\n",
      "num acquisition: 30, time elapsed: 6.99s\n",
      "num acquisition: 31, time elapsed: 7.21s\n",
      "num acquisition: 32, time elapsed: 7.47s\n",
      "num acquisition: 33, time elapsed: 7.70s\n",
      "num acquisition: 34, time elapsed: 7.96s\n",
      "num acquisition: 35, time elapsed: 8.18s\n",
      "num acquisition: 36, time elapsed: 8.44s\n",
      "num acquisition: 37, time elapsed: 8.70s\n",
      "num acquisition: 38, time elapsed: 8.94s\n",
      "num acquisition: 39, time elapsed: 9.21s\n",
      "num acquisition: 40, time elapsed: 9.49s\n",
      "num acquisition: 41, time elapsed: 9.77s\n",
      "num acquisition: 42, time elapsed: 10.02s\n",
      "num acquisition: 43, time elapsed: 10.31s\n",
      "num acquisition: 44, time elapsed: 10.56s\n",
      "num acquisition: 45, time elapsed: 10.80s\n",
      "num acquisition: 46, time elapsed: 11.07s\n",
      "num acquisition: 47, time elapsed: 11.34s\n",
      "num acquisition: 48, time elapsed: 11.61s\n",
      "num acquisition: 49, time elapsed: 11.91s\n",
      "num acquisition: 50, time elapsed: 12.18s\n",
      "num acquisition: 51, time elapsed: 12.47s\n",
      "num acquisition: 52, time elapsed: 12.74s\n",
      "num acquisition: 53, time elapsed: 13.04s\n",
      "num acquisition: 54, time elapsed: 13.39s\n",
      "num acquisition: 55, time elapsed: 13.70s\n",
      "num acquisition: 56, time elapsed: 13.96s\n",
      "num acquisition: 57, time elapsed: 14.25s\n",
      "num acquisition: 58, time elapsed: 14.52s\n",
      "num acquisition: 59, time elapsed: 14.80s\n",
      "num acquisition: 60, time elapsed: 15.10s\n",
      "num acquisition: 61, time elapsed: 15.38s\n",
      "num acquisition: 62, time elapsed: 15.65s\n",
      "num acquisition: 63, time elapsed: 15.93s\n",
      "num acquisition: 64, time elapsed: 16.23s\n",
      "num acquisition: 65, time elapsed: 16.54s\n",
      "num acquisition: 66, time elapsed: 16.82s\n",
      "num acquisition: 67, time elapsed: 17.16s\n",
      "num acquisition: 68, time elapsed: 17.45s\n",
      "num acquisition: 69, time elapsed: 17.73s\n",
      "num acquisition: 70, time elapsed: 18.02s\n",
      "num acquisition: 71, time elapsed: 18.33s\n",
      "num acquisition: 72, time elapsed: 18.61s\n",
      "num acquisition: 73, time elapsed: 18.88s\n",
      "num acquisition: 74, time elapsed: 19.17s\n",
      "num acquisition: 75, time elapsed: 19.46s\n",
      "num acquisition: 76, time elapsed: 19.76s\n",
      "num acquisition: 77, time elapsed: 20.07s\n",
      "num acquisition: 78, time elapsed: 20.38s\n",
      "num acquisition: 79, time elapsed: 20.67s\n",
      "num acquisition: 80, time elapsed: 20.95s\n",
      "num acquisition: 81, time elapsed: 21.30s\n",
      "num acquisition: 82, time elapsed: 21.58s\n",
      "num acquisition: 83, time elapsed: 21.88s\n",
      "num acquisition: 84, time elapsed: 22.17s\n",
      "num acquisition: 85, time elapsed: 22.46s\n",
      "num acquisition: 86, time elapsed: 22.75s\n",
      "num acquisition: 87, time elapsed: 23.07s\n",
      "num acquisition: 88, time elapsed: 23.40s\n",
      "num acquisition: 89, time elapsed: 23.72s\n",
      "num acquisition: 90, time elapsed: 24.02s\n",
      "num acquisition: 91, time elapsed: 24.34s\n",
      "num acquisition: 92, time elapsed: 24.67s\n",
      "num acquisition: 93, time elapsed: 24.99s\n",
      "num acquisition: 94, time elapsed: 25.34s\n",
      "num acquisition: 95, time elapsed: 25.69s\n",
      "num acquisition: 96, time elapsed: 26.03s\n",
      "num acquisition: 97, time elapsed: 26.32s\n",
      "num acquisition: 98, time elapsed: 26.65s\n",
      "num acquisition: 99, time elapsed: 27.02s\n",
      "num acquisition: 100, time elapsed: 27.36s\n",
      "num acquisition: 101, time elapsed: 27.78s\n",
      "num acquisition: 102, time elapsed: 28.11s\n",
      "num acquisition: 103, time elapsed: 28.47s\n",
      "num acquisition: 104, time elapsed: 28.80s\n",
      "num acquisition: 105, time elapsed: 29.13s\n",
      "num acquisition: 106, time elapsed: 29.48s\n",
      "num acquisition: 107, time elapsed: 29.80s\n",
      "num acquisition: 108, time elapsed: 30.19s\n",
      "num acquisition: 109, time elapsed: 30.55s\n",
      "num acquisition: 110, time elapsed: 30.84s\n",
      "num acquisition: 111, time elapsed: 31.25s\n",
      "num acquisition: 112, time elapsed: 31.58s\n",
      "num acquisition: 113, time elapsed: 31.91s\n",
      "num acquisition: 114, time elapsed: 32.26s\n",
      "num acquisition: 115, time elapsed: 32.65s\n",
      "num acquisition: 116, time elapsed: 33.02s\n",
      "num acquisition: 117, time elapsed: 33.36s\n",
      "num acquisition: 118, time elapsed: 33.72s\n",
      "num acquisition: 119, time elapsed: 34.06s\n",
      "num acquisition: 120, time elapsed: 34.47s\n"
     ]
    }
   ],
   "source": [
    "myBopt.run_optimization(max_iter=max_iter, verbosity=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " \n",
      " Opt found \n",
      "\n",
      "Optimum number of line segments: [612. 310. 842.]\n",
      "Function value: 0.058342840166060575\n"
     ]
    }
   ],
   "source": [
    "print('\\n \\n Opt found \\n')\n",
    "print('Optimum number of line segments:', myBopt.x_opt)\n",
    "print('Function value:', myBopt.fx_opt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "myBopt.plot_convergence()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# perform the fit for the optimum\n",
    "x_opt = list(myBopt.x_opt)\n",
    "x_opt.append(x.min())\n",
    "x_opt.append(x.max())\n",
    "ssr = my_pwlf.fit_with_breaks(x_opt)\n",
    "# predict for the determined points\n",
    "xHat = np.linspace(min(x), max(x), num=10000)\n",
    "yHat = my_pwlf.predict(xHat)\n",
    "\n",
    "# plot the results\n",
    "plt.figure()\n",
    "plt.plot(x, y, 'o')\n",
    "plt.plot(xHat, yHat, '-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.058342840166060575"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
